VPP Pricing II: Mixed Integer Linear Programming

The next two steps are defining a simple VPP contract (or a simplified gas-run power plant) and setting up a mixed integer linear programming optimization (MIP) to calculated the intrinsic value and an upper bound for the extrinsic value based on a Monte-Carlo simulation and assuming perfect foresight. The third step outlined in the next part will then be the “exact” pricing of the extrinsic value using dynamic programming and finite difference methods.

The set-up of the simplified gas-run power plant is similar to the one explained in chapter 4.2.3 of the text-book [1]. In general the power plant has three power output level:

  • Plant is off, P=0
  • Generation at minimum load P=P_{min}
  • Generation at maximum load P=P_{max}

The power plant has a fixed efficiency rate

\zeta=\frac{MWh Power Output}{MWh Heat}.

Ramp rates will be neglected, but the power plant has a minimum uptime t_{up} and a minimum downtime t_{down}. The start-up costs are given by a fixed start-up cost \eta (in €) and the price of the gas needed to produce the start-up heat \theta (in MWh).

The mixed integer linear optimization is running in one hour blocks and is using three decision variables per hour i. The binary decision variable \beta_i is true if the power plant is running at minimum load P_{min} or at maximum load P_{max} and \beta_i is false if the plant is off. The real decision variable 0\le s_i \le 1 is equal to one if the plant is started in hour i, which is implied by the following constraint

\beta_i - \beta_{i-1} \le s_i.

The minimum up-time t_{up} and the minimum down-time t_{down} is a consequence of the constraints

\begin{array}{rcl} \beta_i &\ge& \sum_{t=i-t_{up}+1}^{t=i} s_t \\[7pt] \beta_i &\le& 1-\sum_{t=i+1}^{t=i+t_{down}} s_t \end{array}

The real decision variable 0\le \gamma \le 1 is equal to one if the power plant is running at maximum load P_{max} and zero if the power plant is either running at minimum load P_{min} or if the plant is off, that means

\beta_i \ge \gamma_i

Let P_i be the power price, G_i be the gas price and CO_2^i be the carbon dioxide price at hour i. The objective function is then given by

P\& L = \sum_{t=1}^N\left[\left(\gamma_iP_{max} + P_{min}(\beta_i-\gamma_i)\right) \left(P_i - \frac{G_i+CO_2^i}{\zeta}\right) - s_i\left(\eta + \theta (G_i+CO_2^i)\right)\right]

For a one year span the problem consists of 3\cdot 365 \cdot 24 = 26280 decision variables \{\beta_i, \gamma_i, s_i\} and 4 \cdot 365\cdot 24 = 35040 constraints. This comparable small problem can be solved using e.g. the Gnu Linear Programming Kit (GLPK). For an overview on open source linear/mixed integer programming solver see [2].

The model parameters and the example forward curves are outlined in the previous entry VPP Pricing I. The diagram below shows the intrinsic value and the upper bound for the total value (intrinsic plus extrinsic value) based on Monte-Carlo, perfect foresight and MIP for different  power plant efficiencies \zeta. The parameters of the VPP contract are given by

t_{up}=t_{down}=2h, P_{min}=8MW, P_{max}=40MW, \eta=300 EUR, \theta=20MWh,

the (fixed) carbon dioxide price is 3.0€ per MWh heat.

The source code is available here. It depends on GLPK and the latest QuantLib version from the SVN trunk or the next QuantLib 1.2 release.

It is now quite easy to add and price time-integral constraints, e.g. the following constraint restricts the number of starts within a year to be less than or equal to a given number

\sum_{t=1}^N s_i \le \#Starts.

The following diagram shows the results for \#Starts \le 25 and a minimum load P_{min}=25MW.

The source code is available here. It depends on QuantLib 1.1 and if  you want to generate the plot directly from the C++ program you’ll also need R, RCPP and RInside.

[1] M. Burger, B. Graeber, G. Schindlmayr, Managing Energy Risk, ISDN 978-0-470-ß2962-6

[2] S. R. Thorncraft, Evaluation of Open-Source LP Optimization Codes in Solving Electricity Spot Market Optimization Problems.


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